GraftyVul: Synthesising Insecure Programs Through Real-World Vulnerability Grafting

[2608.27928] GraftyVul: Synthesising Insecure Programs Through Real-World Vulnerability Grafting Skip to main content Search Submit Donate Log in Computer Science > Cryptography and Security arXiv:2608.27928 (cs) [Submitted on 28 Aug 2026] Title: GraftyVul: Synthesising Insecure Programs Through Real-World Vulnerability Grafting Authors: Omri Ram , Mitchell Horner , Ron Van der Meyden , Alsharif Abuadbba , Hammond Pearce View PDF HTML (experimental) Abstract: Vulnerability datasets underpin a wide range of security research, including vulnerability detection, automated remediation, and secure code generation. However, existing datasets sacrifice at least one of three desirable properties: diversity (of language or vulnerability type), reproducibility/executability, or realism. We therefore present GraftyVul, a system that constructs vulnerable programs by grafting real-world vulnerabilities into open-source projects. This grounds the dataset in vulnerabilities observed in real-world contexts while harnessing known good build and test environments, enabling exploit-verification scripts to guarantee that an introduced vulnerability successfully alters a program's behaviour. Using GraftyVul, we generate 212 verified and exploitable vulnerable programs spanning five programming languages (Python, TypeScript, Java, Go, and C#) across 23 CWE categories. To evaluate fidelity, we introduce a language- and context-agnostic semantic embedding that compares vulnerabilities by sink, mechanism and host-feature rather than surface code. This approach outperforms standard code embeddings on cross-language clone and CWE classification. These embeddings demonstrate that GraftyVul samples retain a strong semantic signature to their source vulnerability. We additionally compare GraftyVul against 13 widely used datasets, where it attains competitive diversity while being the only reproducible-exploit dataset with broad language and CWE coverage. Finally, we illustrate GraftyVul's practical utility through an industrial case study evaluating a production vulnerability remediation system. Comments: 13 pages, 8 figures Subjects: Cryptography and Security (cs.CR) ; Software Engineering (cs.SE) Cite as: arXiv:2608.27928 [cs.CR] (or arXiv:2608.27928v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.27928 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hammond Pearce [ view email ] [v1] Fri, 28 Aug 2026 05:10:49 UTC (448 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CR < prev | next > new | recent | 2026-08 Change to browse by: cs cs.SE References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• GraftyVul: Synthesising Insecure Programs Through Real-World Vulnerability Grafting#779831
• LLMVul: A Vulnerability-Labeled Dataset of LLM-Generated C/C++ Functions from Real Production Repositories#673585
• LLMVul: A Vulnerability-Labeled Dataset of LLM-Generated C/C++ Functions from Real Production Repositories#998519
• FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction#216739
• SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair#120607